Anterior Cruciate Ligament Graft Fixation—A Myth Busted?
Bibliographic record
Abstract
Anterior cruciate ligament graft fixation has become one of the most investigated topics in the sports traumatology literature. With over 400 publications within the past decade, a plausible explanation for the popularity of the topic would be that anterior cruciate ligament graft fixation represents an obvious clinical problem. Yet this does not seem to be the case. We set out to analyze the veracity of the notion that the fixation site is the weak link in a reconstructed knee in the early postoperative period. A mere temporal association is found between the first clinical reports on increased anterior tibial translation relative to the femur with soft-tissue grafts and the first pullout studies reporting lower ultimate failure loads with such grafts. This association was sufficient to convince the orthopaedic community at large that actual causality exists between soft-tissue graft fixation failure and increased knee laxity during healing. Thus the concept of "graft slippage" was born. Even with the imminent risk of being misconstrued as contentious, we submit that the entire concept of graft slippage is a myth, founded on poor scientific practice and affected by commercial bias. As a way forward, clinically important phenomena should be demonstrated through experiments with clear and sound clinical endpoints. As for preclinical studies, although they are indisputably helpful in the elaboration of such phenomena, serious hazards lie in declaring them a sufficient scientific basis for new research or, worse, for clinical standards of care. More importantly, no matter how sophisticated or fascinating their methodology, preclinical studies do not relieve us from the necessity and duty of proving our theories, whenever possible, with randomized controlled trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".